Perioperative Effects of Surgery, Anesthesia and Analgesics Associated with Cancer Progression: A Review
Bibliographic record
Abstract
One of the most common treatments available for cancer patients is surgical removal of the malignant tumor; its long-term implications, however, are still little-known. The purpose of this review is to look at the perioperative effects and determine if there is any correlation between surgery, anesthetics and analgesics, and cancer progression, in the form of cancerous tumor growth and progression and patient survival, within the Puerto Rican population. A retrospective literature review was conducted. Current data suggest that surgery is associated with an increase in cancer proliferation and metastasis, for various reasons such as angiogenesis enhancement and bloodstream migration. Also, it was found that some anesthetics and analgesics have been associated with cancer progression, based on the peri- and postoperative immune status of the patient. Thiopental, ketamine, isoflurane, halothane and some opioids were positively correlated with cancer progression given their role in immunosuppression; while propofol, lidocaine, ropivacaine and bupivacaine were negatively correlated with tumor progression given their immune enhancement. Others, like sevoflurane, nitrous oxide, and etomidate showed inconclusive correspondence. Therefore, it was concluded that immune system boosting anesthetics and analgesics can reduce cancer progression in a patient that has undergone surgical resection. For further research and since the available data are not extensive, other variables such as age, sex, stressors and comorbidities could be considered to better understand the mechanism in which the chemicals hereby studied can cause cancer progression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".